During transmission, a signal picks up noise interference. It is desirable to suppress or remove the noise in a received signal so as to improve the quality of the received signal. In order to suppress or remove noise from the received signal, the noise spectrum of the received signal is first estimated.
Conventional methods for estimating the noise spectrum of a speech signal use a voice activity detector (VAD) to identify frames of the signal that do not contain any speech. The signal amplitude in each of these frames is indicative of the noise amplitude in that frame. Hence by measuring the signal amplitude in such frames, the receiver can estimate the noise spectrum of the speech signal and track changes in it.
More advanced methods make use of “minimum statistics” for estimating the noise spectrum of a speech signal. Minimum statistics methods track the minimum signal amplitude in the speech signal. The minimum signal amplitude occurs at frames/bins which do not contain any speech. In these frames/bins the signal amplitude is indicative of the noise amplitude in that frame/bin. Hence minimum statistics methods track the noise spectrum in a speech signal.
Both the conventional methods and the more advanced methods discussed above rely on occasional short time intervals in the time-frequency plane of the signal spanning the entire frequency spectrum in which there are no desired signal (i.e. speech) components to the signal. Furthermore these algorithms estimate the noise spectral amplitude of each frequency bin independently of other frequency bins. Hence the methods are able to measure the signal amplitude in these short time intervals and use these measurements to estimate the noise spectrum.
FM (frequency modulated) signals often contain music. Typically, frames in a music signal in which there is no music component to the signal are infrequent. This characteristic, however, is less evident in higher frequencies. In some cases the music signal is uninterrupted in time along its entire duration. Consequently, the above described methods of estimating the noise spectrum are unlikely to be accurate for music signals. In the case of a minimum statistics method, the method is usually set up such that a parameter defines the maximum duration of continuous presence of a signal. In the case of a music signal, this parameter would be high to take into account the fact that pauses in the music signal are infrequent. However the parameter taking a high value reduces the ability of the method to accurately and quickly track changes in the noise spectrum because the measurements taken of the signal indicative of noise estimates are infrequent. Additionally, if a portion of the music signal lasts uninterrupted for longer than the specified maximum duration, the method will measure the minimum of the music signal and use this to estimate the noise spectrum. If the estimated noise spectrum is then used to suppress or remove the noise component from the signal, the resulting signal will be severely distorted.
Typical noise compensation methods used to improve the quality of FM signals are directed either at improving the hardware receiving the FM signal or at using software for post-processing of the FM signal to hide the effects of noise in the signal. In general, these methods are not directed at estimating the noise spectrum and suppressing or removing it from the signal. Improvements to the receiving hardware are aimed at improving the received signal strength indication (RSSI). Software techniques include soft mute, adaptive low-pass filtering and mono/stereo blending. These software techniques all remove the wanted signal in addition to the unwanted noise. For example, low-pass filtering suppresses the high frequency content of the signal and allows the low frequency content to pass through. Since most of the wanted signal occupies lower frequencies, the overall signal to noise ratio (SNR) increases. However, if there is substantial high frequency content to the wanted signal then the quality of the signal will reduce considerably since this content is suppressed by the low-pass filter. Additionally, this method does not remove the noise from the frequency bands passed by the low-pass filter. Although widely implemented, these software post-processing methods do not improve the quality of the signal as much as would be possible by estimating the noise spectrum and suppressing or removing it from the signal.
There is thus a need for an improved method of estimating the noise spectrum of a signal, particularly of an FM signal.